Publication: Predictive performance of a multivariable difficult intubation model for obese patients
Issued Date
2018-08-01
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ISSN
19326203
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2-s2.0-85052786590
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Mahidol University
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SCOPUS
Bibliographic Citation
PLoS ONE. Vol.13, No.8 (2018)
Suggested Citation
Arunotai Siriussawakul, Patcharee Maboonyanon, Subongkot Kueprakone, Suthasinee Samankatiwat, Chulaluk Komoltri, Chayanan Thanakiattiwibun Predictive performance of a multivariable difficult intubation model for obese patients. PLoS ONE. Vol.13, No.8 (2018). doi:10.1371/journal.pone.0203142 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/44706
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Title
Predictive performance of a multivariable difficult intubation model for obese patients
Abstract
© 2018 Siriussawakul et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Background A predictive model of scores of difficult intubation (DI) may help physicians screen for airway difficulty to reduce morbidity and mortality in obese patients. The present study aimed to set up and evaluate the predictive performance of a newly developed, practical, multivariate DI model for obese patients. Methods A prospective multi-center study was undertaken on adults with a body mass index (BMI) of 30 kg/m 2 or more who were undergoing conventional endotracheal intubation. The BMI and 10 preoperative airway tests (namely, malformation of the teeth in the upper jaw, the modified Mallampati test [MMT], the upper lip bite test, neck mobility testing, the neck circumference [NC], the length of the neck, the interincisor gap, the hyomental distance, the thyromental distance [TM] and the sternomental distance) were examined. A DI was defined as one with an intubation difficulty scale (IDS) score 5. Results The 1,015 patients recruited for the study had a mean BMI of 34.2 (standard deviation: 4.3 kg/m 2 ). The proportions for easy intubation, slight DI and DI were 81%, 15.8% and 3.2%, respectively. Drawing on the results of a multivariate analysis, clinically meaningful variables related to obesity (namely, BMI, MMT, and the ratio of NC to TM) were used to build a predictive model for DI. Nevertheless, the best model only had a fair predictive performance. The area under the receiver operating characteristic curve (AUC) was 0.71 (95% confidence interval 0.68–0.84). Conclusions The predictive performance of the selected model showed limited benefit for preoperative screening to predict DI among obese patients.